{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任务3：\n",
    "输入1（cycle）2（EMG_retus_femoris_l=emg_rf_l）3（EMG_lat_hams_l=emg_lh_l），输出 7（LKneeAngle = ka_l）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据不进行分类，抽取4个作测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "# 可视化\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "Visdom_Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Visdom\\\\'\n",
    "if not os.path.exists(Visdom_Dir):\n",
    "    os.makedirs(Visdom_Dir)\n",
    "image_Dir ='/mnt/data/ZP_Project/Muscle/muscle/image_Mission3_FC_all_together'\n",
    "if not os.path.exists(image_Dir):\n",
    "        os.makedirs(image_Dir)\n",
    "# image_Dir ='G:\\科研学习\\肌电信号\\Code\\musle force\\image_Mission3_FC\\\\'\n",
    "# if not os.path.exists(image_Dir):\n",
    "#         os.makedirs(image_Dir)\n",
    "\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "Dir = '/mnt/data/ZP_Project/Muscle/muscle/Transed_Data/'\n",
    "filenames = os.listdir('/mnt/data/ZP_Project/Muscle/muscle/Transed_Data')\n",
    "# Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\\\'\n",
    "# filenames = os.listdir('G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data')\n",
    "\n",
    "data_rf = []\n",
    "data_lh = []\n",
    "data_kal = []\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = scio.loadmat(Dir + str(filename))\n",
    "    #print(data.keys())  #dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n",
    "    data1 = data['emg_rf_l']\n",
    "    #data1 = data1.squeeze()\n",
    "    data2 = data['emg_lh_l']\n",
    "    data3 =  data['ka_l']\n",
    "    #data2 = data2.squeeze()\n",
    "    data_rf.append(data1)\n",
    "    data_lh.append(data2)\n",
    "    data_kal.append(data3)\n",
    "\n",
    "data_rf = np.array(data_rf)\n",
    "data_lh = np.array(data_lh)\n",
    "data_kal = np.array(data_kal)\n",
    "set=MuscleData2(data_rf,data_lh,data_kal)\n",
    "train_set,test_set =  torch.utils.data.dataset.random_split(set, [28, 4])\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "\n",
    "Net = FC_Net2()\n",
    "Net.apply(weights_init)\n",
    "Net.to(device)\n",
    "\n",
    "train_loader=torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False)\n",
    "test_loader=torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False)\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 10000\n",
    "\n",
    "optimizer = torch.optim.SGD(Net.parameters(),\n",
    "lr=0.0000015,momentum=0.9)\n",
    "vizx = 0\n",
    "#开始训练\n",
    "for epoch in range(epoch_num):\n",
    "        #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "        for batch in train_loader:\n",
    "                vizx+=1\n",
    "                data,data2,label = batch\n",
    "                data = torch.as_tensor(data)\n",
    "                data2 = torch.as_tensor(data2)\n",
    "                data = data.type(torch.FloatTensor)\n",
    "                data2 = data2.type(torch.FloatTensor)\n",
    "                \n",
    "                #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                label =torch.as_tensor(label)\n",
    "                label = label.squeeze(2)\n",
    "                #print(label.shape)\n",
    "                label = label.type(torch.FloatTensor)\n",
    "                data = data.cuda()\n",
    "                data2 = data2.cuda()\n",
    "                label = label.cuda()\n",
    "                #print(data.shape)\n",
    "                pred = Net(data,data2)\n",
    "                #print(pred.shape)\n",
    "                #print(label.shape)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                #print('pred',pred.shape)\n",
    "                #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                loss = criterion(pred,label)\n",
    "                \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                viz_batch[0].line([float(loss)],[vizx],win='loss'+str(0), name='loss'+str(0), update='append',opts=dict(title='train_loss'))\n",
    "                optimizer.step()\n",
    "                #n +=1\n",
    "                #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i =0\n",
    "#测试\n",
    "for batch in test_loader:\n",
    "        data1,data2,label = batch\n",
    "        data1 = torch.as_tensor(data1)\n",
    "        data2 = torch.as_tensor(data2)\n",
    "        data1 = data1.type(torch.FloatTensor)\n",
    "        data2 = data2.type(torch.FloatTensor)\n",
    "        data1 = data1.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        data2 = data2.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        # label =label.to(device)\n",
    "        #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "        pred = Net(data1,data2)\n",
    "        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "        pred = pred.cpu()\n",
    "        pred = pred.data.numpy()\n",
    "        pred=np.squeeze(pred)\n",
    "        label=np.squeeze(label)\n",
    "        data1 =data1.cpu()\n",
    "        data2 =data2.cpu()\n",
    "        data1 = np.squeeze(data1)\n",
    "        data2 = np.squeeze(data2)\n",
    "        #利用plot画图\n",
    "        plt.figure(i)\n",
    "        plt.title('test_sample'+str(i))\n",
    "        plt.plot(pred, label='pred')\n",
    "        plt.plot(label, color='red', label='GroundTruth')\n",
    "        plt.plot(data1*100, color='black', label='data1')\n",
    "        plt.plot(data2*100, color='black', label='data2')\n",
    "        plt.legend()\n",
    "        #增\n",
    "        figname ='./image_Mission3_FC_all_together/imag_ {}.jpg'.format(i)\n",
    "        plt.savefig(figname)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "        i +=1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #用于打印网络里权重。默认注释掉\n",
    "# Net.state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3b95d8d6e207246d09e19daee9bbc5bbe4502ff924268508716ea3969933742"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
